Modeling Concept Activation in Working Memory during Online Sentence Processing

Patrick Plummer, University of California, San Diego

Hsueh-Cheng Wang, University of Massachusetts at Boston

Marc Pomplun, University of Massachusetts at Boston

Yuhtsuen Tzeng, National Chung Cheng University

Keith Rayner, University of California, San Diego

Abstract

There have been several computational alternatives to the cloze task (Taylor,
1953) intended to approximate word predictability effects on eye movements during
reading. In this study, we implement a computational model that instantiates each
content word in a sentence as an input that activates semantic concepts in
working memory. The predictability of a word is then determined by the extent to
which its corresponding semantic representation is associated with the network of
concepts already active in working memory from the preceding context. The
computation of concept activation is based on a connectionist model (Landscape
model, see van den Broek, 2010). Latent semantic analysis (LSA) is used to
establish connections between words and simulate the long-term semantic
associations among concepts (Landauer & Dumais, 1997). This model provides a
means of investigating how language comprehension and eye movement behavior are
affected by the activation of concepts in working memory.